Disruption of Rolandic Gamma-Band Functional
Connectivity by Seizures is Associated with Motor
Impairments in Children with Epilepsy
George M. Ibrahim1,3, Tomoyuki Akiyama2, Ayako Ochi2, Hiroshi Otsubo2, Mary Lou Smith2,6,
Margot J. Taylor3,4,5,7, Elizabeth Donner2, James T. Rutka1, O. Carter Snead, III2,3,5, Sam M. Doesburg4,5,7*
1Division of Neurosurgery, Hospital for Sick Children, Toronto, Ontario, 2Division of Neurology, Hospital for Sick Children, Toronto, Ontario, 3Institute of Medical Science,
University of Toronto, Toronto, Ontario, 4Department of Medical Imaging, University of Toronto, Toronto, Ontario, 5Neurosciences and Mental Health Program, Hospital
for Sick Children Research Institute, Toronto, Ontario, 6Department of Psychology, University of Toronto, Toronto, Ontario, 7Department of Diagnostic Imaging, Hospital
for Sick Children, Toronto, Ontario
Although children with epilepsy exhibit numerous neurological and cognitive deficits, the mechanisms underlying these
impairments remain unclear. Synchronization of oscillatory neural activity in the gamma frequency range (.30 Hz) is
purported to be a mechanism mediating functional integration within neuronal networks supporting cognition, perception
and action. Here, we tested the hypothesis that seizure-induced alterations in gamma synchronization are associated with
functional deficits. By calculating synchrony among electrodes and performing graph theoretical analysis, we assessed
functional connectivity and local network structure of the hand motor area of children with focal epilepsy from intracranial
electroencephalographic recordings. A local decrease in inter-electrode phase synchrony in the gamma bands during ictal
periods, relative to interictal periods, within the motor cortex was strongly associated with clinical motor weakness. Gamma-
band ictal desychronization was a stronger predictor of deficits than the presence of the seizure-onset zone or lesion within
the motor cortex. There was a positive correlation between the magnitude of ictal desychronization and impairment of
motor dexterity in the contralateral, but not ipsilateral hand. There was no association between ictal desynchronization
within the hand motor area and non-motor deficits. This study uniquely demonstrates that seizure-induced disturbances in
cortical functional connectivity are associated with network-specific neurological deficits.
Citation: Ibrahim GM, Akiyama T, Ochi A, Otsubo H, Smith ML, et al. (2012) Disruption of Rolandic Gamma-Band Functional Connectivity by Seizures is Associated
with Motor Impairments in Children with Epilepsy. PLoS ONE 7(6): e39326. doi:10.1371/journal.pone.0039326
Editor: Shu-min Duan, Zhejiang University School of Medicine, China
Received February 23, 2012; Accepted May 18, 2012; Published June 21, 2012
Copyright: ? 2012 Ibrahim et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This work was supported by the Hospital for Sick Children, Foundation Student Scholarship Program, the Hospital for Sick Children Centre for Brain and
Behavior, the Ontario Brain Institute, the Wiley Family and Jack Beqaj Funds for epilepsy surgery research, the University of Toronto Surgeon-Scientist Program,
and the Royal College of Physicians and Surgeons of Canada Clinician-Investigator Program. The funders had no role in study design, data collection and analysis,
decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: email@example.com
Children with epilepsy are known to exhibit varying levels of
neuropsychological impairments ranging from motor weakness to
deficits in cognition, perception and memory [1,2]. Prolonged
refractory seizures are associated with increased functional
impairment, and early control of epilepsy is imperative to
counteract developmental deficits [3,4]. Furthermore, it is
common for patients with focal epileptogenic lesions to present
with diffuse alterations of cognitive function, which cannot be
exclusively attributed to the location of the lesion. Although recent
research has suggested that performance difficulties associated
with epilepsy may arise due to disruption of functional connectivity
within distributed brain networks [5,6], the mechanism of seizure-
induced network impairment remains unclear.
Initially described in the visual cortex, oscillatory neural
synchronization in the gamma frequency range (.30 Hz) is
thought to dynamically modulate functional connectivity among
neural populations [7,8]. Synchronization of gamma oscillations is
also understood to underlie coordination of activity within
distributed task-dependent neuronal assemblies [9,10] supporting
numerous processes including sensory integration, attention,
action selection as well as learning and response inhibition .
Oscillatory synchronization among brain regions has been
implicated in motor control, and its disturbance has been studied
in clinical populations . Maturation of gamma oscillations is
related to the development of cognition and perception during
childhood and adolescence [13,14], and atypical patterns of
oscillatory coherence are associated with conditions affecting
childhood cognitive development [15,16].
Aberrant brain synchronization has long been thought to play
a critical role in epileptic seizures [17,18] and experimental
observation has confirmed abnormal synchrony within epilepto-
genic brain regions [19,20]. Recent application of graph
theoretical analysis has also revealed that network properties of
functional connectivity are abnormal in epileptic cortex and that
these areas are functionally disconnected from other brain regions
[19,21,22]. Despite convergent evidence implicating network
connectivity in cognitive, perceptual and motor function, their
impairments in clinical populations, and recent findings linking
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oscillatory power to functional difficulties in epilepsy , the
relationship between oscillatory synchrony and functional deficits
in epilepsy remains poorly understood.
The study uniquely investigates relationships among network
synchrony and functional impairment in epilepsy. Specifically, we
evaluate the consequences of uncontrolled epileptic seizures on
motor networks by evaluating their impact on functional
connectivity involving the Rolandic cortex. The identified network
properties are compared with neuropsychological assessments to
identify associations between network connectivity and clinical
motor deficits. Furthermore, to evaluate whether these effects are
due to relations among neural synchrony or rather a reflection of
the location of the epileptogenic cortex, we include patients with
Rolandic and extra-Rolandic epilepsy and adjust for the location
of the intracranial electroencephalographic (iEEG) seizure-onset
zone (SOZ) and/or the epileptogenic lesion on magnetic
resonance imaging (MRI) relative to the Rolandic cortex.
We obtained iEEG recordings from fifteen children undergoing
invasive monitoring for surgical treatment of medically-intractable
focal epilepsy at the Hospital for Sick Children. The underlying
pathology in all cases was focal cortical dysplasia (FCD), as
classified by the International League Against Epilepsy . The
protocol for the analyses described herein was reviewed and
approved by our The Hospital for Sick Children research ethics
board. Retrospective electrophysiological data were reviewed and
no prospective patient consent was required.
The mean age was 11.364.2 years with a mean duration of
epilepsy of 5.363.1 years and a mean daily seizure frequency of
4.864.4 seizures per day. The majority of children (10 patients;
67%) had type II FCD with balloon cells visible on microscopic
examination. Subdural grids were used (median 111 electrodes;
range 98–122). A sample case is presented in Figure 1. There
were no significant differences between children with normal and
abnormal motor function with respect to duration of epilepsy
(p=0.35), seizure frequency (p=0.15), number of distinct seizures
(p=0.56) or size of the SOZ (p=0.17). The distance between the
SOZ and the Rolandic cortex was, however, significantly less in
children with abnormal motor function (p=0.02). None of these
children exhibited post-ictal (Todd’s) paresis.
The technique of subdural grid implantation and intra- and
extra-operative functional mapping of the epileptogenic and
eloquent cortices have been previously described . We used
subdural grids of 4-mm diameter electrodes embedded in
a silicone elastomer sheet with interelectrode distances ranging
from 8 to 10-mm. Patients underwent digitally recorded in-
Montreal, QC, Canada) with a sampling rate of 1 kHz and
anti-aliasing filter at 300 Hz (Butterworth, 220 dB/oct) applied
prior to sampling. An averaged reference was selected by clinical
electrophysiologists from two channels in a relatively inactive
area of the grid during seizures, which was also distant from
Rolandic cortex. Children were included in the study if the
subdural grid covered the motor cortex and the hand motor area
was reliably identified by cortical stimulation.
Ictal and interictal epochs were selected based on iEEG
tracings. Ictal periods were of variable length and were comprised
of rhythmic iEEG activity demonstrating evolution over time and
associated with clinical seizures. Interictal epochs were each two
minutes in length and selected at least an hour apart from ictal
events. The interictal periods were chosen by experienced
electrophysiologists as representative background activity, which
in most cases, included interictal epileptic discharges. The iEEG
sections were exported as European Data Format Plus (EDF+) files
 and imported into MATLAB software for subsequent
analyses (The MathWorks, Natick, MA, USA). At least three
epochs of each type were analyzed for each patient and the mean
phase-locking and clustering values were calculated.
Phase Synchronization Analysis
To determine synchronization within the motor cortex, we
extracted a Rolandic region of interest (ROI) including data from
the electrode determined by extra-operative stimulation to be over
the hand motor area and its adjacent electrodes (e.g. a 3 by 3
electrode montage centred over the motor hand area). An internal
control was chosen by defining another 3 by 3 montage of
electrodes at least three electrodes away from the motor hand area
and as equidistant as possible to the SOZ. To investigate long-
range phase synchronization involving motor cortex, a secondary
analysis was performed including all pairs of electrodes within the
grid. The data were band-pass filtered digitally at 1 Hz intervals
between 4 and 300 Hz with a notch filter applied to all resonance
frequencies of 60 Hz to exclude line noise. The analytical signal of
the filtered waveform for each ictal and interictal epoch f(t) was
calculated to obtain the instantaneous phase w t ð Þ, where~f f t ð Þis the
Hilbert transform of f t ð Þ:
f t ð Þ~f t ð Þzi~f f t ð Þ~A(t)eiw t ð Þ
Inter-electrode phase synchronization was quantified using phase-
locking values (PLVs). PLVs were calculated by comparing the
instantaneous phases of signals recorded by pairs of electrodes
across time . As we hypothesized that seizures alter the
functional connectivity of eloquent cortex, an average PLV value
for each pair-wise electrode relation was derived for the entire
course of each ictal and interictal epoch.
PLVs range between 0 (random phase difference) and 1
(maximum phase-locking). To determine inter-electrode synchro-
ny within the motor cortex, the PLV values associated with all
pair-wise comparisons of the electrodes within the motor ROI
were averaged across each frequency band. The derived PLV
was subsequently averaged across defined frequency bands: delta
(1–4 Hz), theta (5–8 Hz), alpha (8–13 Hz), beta (14–30 Hz),
gamma1 (36–44 Hz),gamma2
150 Hz),HFO1 (151–200 Hz),
HFO3 (251–300 Hz). In order to quantify the disruption of
functional connectivity associated with seizures, we subtracted
the mean interictal phase-locking from ictal values within each
analyzed frequency range, creating a composite variable de-
scribing the ictal phase desynchronization (hereafter referred to
as ‘ictal desynchronization’).
Graph Theoretical Analysis
To assess network connectivity involving motor cortex, we
performed graph theoretical analysis using the Brain Connectivity
Toolbox . Each electrode on the grid was defined as a ‘node’
and the calculated PLV values represented the ‘edge’ weights,
creating a weighted undirected network. A key measure of
networks is the clustering coefficient . This is defined by the
ratio of the number of connections between the neighbours of
a node and the number of all the possible connections between its
neighbours . By providing an indication of the embeddedness
of a single node, clustering coefficients quantify the degree of
connectivity within the synchronization network. We constructed
Rolandic Disconnection and Motor Deficits
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our graphs using weighted undirected edges defined by PLVs and
therefore derived a weighted clustering coefficient, representing
the average ‘intensity’ of triangles around a node . A
frequency of 80 Hz was selected to investigate clustering in the
gamma-band, as this was consistent with the frequency range
which showed the strongest relationship between ictal desynchro-
nization and motor function in the ROI analysis. Once the
connectivity matrix of the entire grid was analyzed, all possible
connections between the electrode recording from the motor
cortex (the hand motor node) and the remainder of the electrodes
(i.e. all functional neighbours) were considered in the analysis and
the clustering coefficient was extracted . As with the analysis of
phase synchronization within Rolandic cortex, we subtracted the
interictal clustering values from ictal values in order to index the
seizure-induced disturbance of connectivity within functional
systems involving motor cortex. This composite variable is termed
The majority of children underwent a battery of neuropsy-
chological testing including two motor-related tasks: the grooved
pegboard (N=10)  and finger tapping tasks (N=9) .
Non-motor aspects of the children’s function were assessed with
the matrix reasoning (N=13) and vocabulary subsets (N=12) of
the Wechsler Intelligence Scale for Children . Age- and
gender-adjusted z-scores were derived [35,36]. Children were
dichotomized as having normal (N=9) or abnormal (N=6)
motor function according to their neuropsychological evalua-
tions with those performing under one standard deviation from
the mean on the grooved pegboard task and/or with gross
motor weakness of the contralateral hand on neurological
examination. The grooved pegboard task was used to define
abnormal hand function as it is the more complex motor task,
requiring both speed and dexterity. All testing was administered
and interpreted by neuropsychologists and neurologists during
Data are presented as means with error bars representing the
standard deviation unless stated otherwise. Binary and dichoto-
mized categorical variables were analyzed using the two-tailed
Fisher’s exact test. The means of continuous variables were
compared using the nonparametric randomization test, which
employs resampling techniques to yield exact significance levels
. To adjust for confounders, a one-way analysis of variance
(ANOVA) or analysis of covariance (ANCOVA) was performed
for categorical and continuous variables respectively. A multivar-
iate logistic binary regression was also performed with variables
selected for inclusion based on a priori hypotheses. Outcomes were
considered statistically significant at a p,0.05. Statistical analysis
was performed using SAS Statistical Software 9.3 (Cary, North
Ictal Gamma-band Desynchronization and Motor
There was a trend towards a significant difference between ictal
PLVs in the Rolandic ROI (3 by 3 electrode montage) in children
with normal and abnormal motor function, most notably in the
gamma2 (p=0.053) and gamma3 (p=0.06) frequencies. When we
tested our composite variable, ‘ictal desynchronization’ – which
has the notable advantage of accounting for baseline (interictal)
differences in Rolandic connectivity between subjects – those with
motor deficits were significantly more likely to have ictal
desynchronization within the contralateral Rolandic ROI than
those without deficit. This was observed across numerous
frequency bands and was most significantly expressed in the
gamma3 band (81–150 Hz: p,0.01; Figure 2). Because this
frequency band yielded the greatest difference, it was selected for
comparison with neuropsychological testing.
Performance on the grooved pegboard task of fine motor
dexterity of the hand contralateral to recording showed a strong
linear correlation with extent of ictal desynchronization at the
Figure 1. Intraoperative photograph showing placement of grid over right hemisphere. (A) Asterisk indicates motor hand area as
determined by cortical stimulation. (B) Three-dimensional reconstruction showing grid (light pink dots), MRI lesion (dark pink area),
magnetoencephalographic (MEG) cluster (green dots). Square shows 363 montage used for PLV analysis.
Rolandic Disconnection and Motor Deficits
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gamma3 band (Pearson coefficient: 0.62; p=0.05; Figure 3).
The correlation of ictal desynchronization in the gamma3
frequency band with fine motor dexterity outcomes for the hand
ipsilateral to recording showed a weaker and non-significant
trend (Pearson coefficient: 0.44; p=0.18) and correlation with
non-motor neuropsychological function, the verbal and matrix
reasoning subtests of WISC-IV showed no association (Pearson
coefficients 20.18 and 0.19 respectively; p=0.57 and 0.52
respectively). There was no significant correlation between ictal
desynchronization and the tapping task in the contralateral or
ipsilateral hands (Pearson correlations: 0.41 and 0.02; p=0.27
and 0.96 respectively).
When adjusting for the presence of the iEEG defined seizure-
onset zone (SOZ) and/or MRI lesion within the motor cortex
using a two-way ANOVA, we found that significant differences in
ictal desynchronization in the gamma3 frequency band (81–
150 Hz) which exhibited the most significant association with
motor function remained between children with normal and
abnormal contralateral hand function (F-value=6.48; p=0.03).
Furthermore, there was no interaction between ictal desynchro-
nization in this frequency band and the presence of iEEG defined
SOZ and/or MRI lesion (F-value=0.73; p=0.41). We included
the frequency yielding the strongest difference between normal
and abnormal motor function (gamma3, 81–150 Hz) in a multi-
Figure 2. Differences in Rolandic phase-locking between ictal and interictal epochs (i.e. ictal phase synchronization) between
children with normal and abnormal motor function across defined frequency bands. Children with motor deficits had ictal
desychronization (relative to interictal period), most significantly in the gamma3 (81–150 Hz) band.
Figure 3. Linear regression of (A) motor tasks and (B) non-motor tasks with differences in phase-locking between ictal and interictal
epochs. Extent of ictal desynchronization (relative to interictal epochs) was significantly correlated with degree of neuropsychological impairment
for the grooved pegboard task in the hand contralateral to recording, but not the ipsilateral hand or non-motor tasks.
Rolandic Disconnection and Motor Deficits
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variate logistic regression model and found that gamma-range ictal
desynchronization in that frequency band was a stronger in-
dependent predictor of motor deficit (OR 29.97; 95% CI 1.41–
637.67) than the presence of the iEEG-defined SOZ or MRI lesion
within the motor cortex (OR 3.45; 95% CI 0.11–112.61).
When we adjusted for epilepsy duration using ANCOVA, we
found that the association between ictal desynchronization at the
81–150 Hz band and abnormal contralateral motor function
remained significant (F-value=13.79; p,0.01). Longer duration
of epilepsy (greater than 6 years) was also independently associated
with worse neuropsychological outcomes on the grooved pegboard
task (F-value 8.70; p=0.03), but not the finger-tapping task (F-
value=0.86; p=0.40). There was no significant interaction
between ictal desychronization and epilepsy duration (F-val-
ue=3.12 and 0.29; p=0.18 and 0.61 on grooved pegboard and
finger-tapping tasks respectively).
In our internal control montage, located at least three electrodes
away from the hand motor area and equidistant from the SOZ,
there was no difference in ictal desynchronization between
children with normal and abnormal motor function at any
frequency band (delta p=0.38; theta p=0.10; alpha p=0.09; beta
p=0.29; gamma1 p=0.25; gamma2 p=0.29; gamma3 p=0.64;
HFO1 p=0.80; HFO2 p=0.78; and HFO3 p=0.96). Further-
more, there was no significant correlation between ictal desyn-
chronization at the gamma3 frequency band of the control
montage and any neuropsychological test (data not shown).
Graph Theoretical Analysis
Analysis of graph theoretical properties of Rolandic cortex
connectivity involving the entire electrode grid revealed that
patients with hand motor deficits had significantly higher ictal
declustering at 80 Hz involving the hand motor area of the
contralateral Rolandic cortex compared relative to children
without motor impairment, despite no significant differences in
ictal and interictal clustering between these two groups (Figure 4).
Of note, however, the trend towards significance during the ictal
period (p=0.06) suggests that seizures are driving the differences
observed. Neuropsychological outcomes for the hand tapping task
for the hand contralateral to recording showed a strong linear
correlation with extent of ictal declustering at 80 Hz (Pearson
coefficient: 0.74; p=0.04; Figure 5). There was no significant
association between the finger tapping of the ipsilateral hand, or
non-motor tasks, and ictal declustering of gamma connectivity. For
the grooved-pegboard test, there was a significant association
between ictal declustering and outcome for the ipsilateral hand
(Pearson coefficient: 0.66; p=0.04), but not the contralateral hand
(Pearson coefficient: 0.44; p=0.23) or non-motor tasks.
After adjusting for the presence of the iEEG-defined SOZ and/
or MRI lesion within the hand-motor area of the Rolandic cortex
using a two-way ANOVA, the decrease in clustering coefficient at
80 Hz between the ictal and interictal period remained signifi-
cantly different between children with normal and abnormal
contralateral hand function (F-value=6.23; p=0.03). There was
no significant interaction between iEEG-defined SOZ and/or
MRI lesion location in Rolandic cortex and abnormal contralat-
eral hand function (F-value=0.02; p=0.90). We also adjusted for
epilepsy duration using ANCOVA and found that the observed
differences in ictal declustering between children with normal and
abnormal motor functions remained significant (F-value=12.96;
p,0.01). There was no significant interaction between duration of
epilepsy and ictal declustering properties (F-value=0.15; p=0.30).
In a multivariate logistic regression model, neither a binarized ictal
declustering variable nor the presence of the SOZ/MRI lesion in
Rolandic cortex was predictive of abnormal hand function.
The current study uniquely demonstrates that seizures alter the
functional connectivity of eloquent cortical areas and that these
alterations are predictive of clinical neurological deficit. We also
provide direct evidence that seizure-induced alterations of
connectivity in functional networks, which may be distant from
iEEG-defined SOZ or presumptive epileptogenic MRI lesions, are
associated with neurological impairments. We also provide the first
evidence that invasion of function-specific areas of eloquent cortex
by ictal connectivity dynamics are selectively related to impair-
ment of the relevant functional domain (e.g. ictal desynchroniza-
tion of motor cortex is selectively relevant to motor impairment).
Hand motor function was selected to test this hypothesis as it
represents a relatively simple, robust network, for which the
implicated cortical regions are reliably identified through cortical
stimulation . Based on our findings, we speculate that ictal
phase desynchronization and disruption of functional connectivity
within a variety of distributed brain networks may underlie the
broad spectrum of impairments affecting children with epilepsy.
This view is consistent with EEG evidence linking gamma-band
synchronization to the formation of distributed neuronal coalitions
supporting a variety of cognitive and perceptual processes [38–42]
as well findings of transient ictal desynchrony during aberrant
emotional behaviour . The observed association between ictal
reduction gamma-band synchrony within Rolandic cortex and
motor deficit may therefore represent a mechanism through which
epileptic seizures exert long-lasting effects on cortical network
dynamics and consequently neuropsychological function.
It remains unclear why in the present study seizure-induced
changes in functional connectivity were found to be independently
associated with motor weakness, although this finding is supported
by clinical associations between longer duration of epilepsy and
increased baseline functional impairment. One possible explana-
tion is that prolonged desynchronization and disconnection of
functional networks may facilitate network plasticity within the
Rolandic cortex, resulting in clinical motor deficit. It has been
previously shown that the capacity of individual neurons to exhibit
adaptive changes or plasticity is influenced by gamma synchro-
nization [44,45]. Furthermore, coherence of oscillatory gamma-
band EEG activity has been previously studied as a basis for
cognitive processes necessitating neuronal plasticity, such as
learning and memory [45–47]. A critical implication of our
findings is that aberrant gamma synchrony may act to patholog-
ically decorrelate neurons comprising a functional circuit, resulting
in long-lasting disruptions in connectivity and thus motor
Another explanation for our findings involves the role of
interictal discharges in disrupting networks beyond the ictal
period, further contributing to network destabilization. Using
EEG-correlated functional MRI, it has been reported that
interictal epileptic discharges disrupt resting-state networks in
a manner analogous to task performance . However, it has
also been shown that patients with epilepsy exhibit resting-state
network impairments during interictal periods without interictal
epileptic discharges and that functional connectivity is negatively
correlated with disease duration . Based on the findings of the
current study, we hypothesize that dysfunctional network in-
tegration may be related to repeated ictal desychronization and
functional disconnection within brain networks supporting motor
function. This viewpoint is further buttressed by recent demon-
stration that phase correlations among gamma oscillations in
distributed neuronal coalitions contribute to the formation of task
specific network interactions involving the motor system .
Rolandic Disconnection and Motor Deficits
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A wealth of literature has also recently emerged suggesting that
pathological high frequency oscillations (pHFOs; above 80 Hz) are
a signature of cortical epileptogenicity [50,51]. One study showed
that the presence of ictal motor symptoms was related more to
pHFO amplitude in Rolandic cortex than in the SOZ, and that
augmentation of ripple-band pHFOs (80–200 Hz) occurred
approximately 400 ms prior to EMG onset of ictal motor
phenomenon . In contrast to studying oscillation amplitude,
we examined phase-locking synchrony and graph theoretical
properties of gamma-band networks and showed that ictal
disturbances in connectivity are associated with baseline motor
deficit. Furthermore, we demonstrated that the magnitude of
motor impairment was correlated with the extent of desynchro-
nization and functional disconnection within Rolandic cortex.
Using multivariate analysis, we have also shown that these findings
are more predictive of deficit than epileptogenicity (SOZ presence)
in the Rolandic cortex. It is also interesting that in the current
study, the most significant frequency was the high-gamma band
(81–150 Hz) (p,0.01), suggesting that ictal desychronization was
strongest within the gamma-band, which has been reliably
implicated in the formation of networks supporting cognition,
perception and motor control. Finally, while previous studies have
shown that the SOZ is itself functionally disconnected , we
show that ictal disconnection of eloquent cortical areas in-
dependent of the SOZ location is associated with neurological
deficits. We speculate that this may be a leading mechanism for
neurological and cognitive impairment in children with epilepsy.
The present study possesses numerous advantages over previous
works. Importantly, large subdural grids were used for electro-
corticography, allowing us to capture a greater number of nodes
and to more accurately apply graph theoretical analyses to map
local and inter-regional connectivity involving Rolandic cortex.
Secondly, electrophysiological synchrony and graph theoretical
properties were associated with clinical and neuropsychological
Figure 4. Graph theoretical analysis-based topographic mapping of grid showed significant local declustering in the Rolandic
cortex during the ictal period relative to interictal epoch (ictal minus interictal) at 80 Hz in children with abnormal motor function.
There was no significant difference between the two groups of children interictally and there was a trend towards more declustering in the ictal
period (p=0.06). There was a significant difference between the extent of ictal declustering (relative to interictal epochs) in children with normal and
abnormal motor function.
Figure 5. Linear regression of (A) motor tasks and (B) non-motor tasks with differences in clustering between ictal and interictal
epochs. Extent of Rolandic ictal declustering (relative to interictal epochs) significantly correlated with the degree of motor impairment based on the
Finger tap test in the contralateral, but not ipilateral hand and did not show significant correlation with non-motor deficits.
Rolandic Disconnection and Motor Deficits
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impairments. Finally, the epileptogenic pathology was the same
(focal cortical dysplasia) for all children. Our main limitation is the
absence of a control group, which has been previously reported for
intracranial EEG .
The current report is the first to bridge the gap in knowledge
between disturbances in functional connectivity caused by epilepsy
and clinical impairments observed in affected children. We show
that seizure-induced desychronization of Rolandic cortex is
associated with contralateral motor hand deficits independently
of the location of the SOZ. Furthermore, ictal disruptions in
functional connectivity, shown by local declustering of the
Rolandic cortex, are also associated with motor deficit following
adjustment for SOZ location. Finally, our study has the advantage
of correlating observed seizure-induced changes with clinical and
neuropsychological outcomes, rather than statistical differences in
BOLD signal on fMRI. These findings were highly significant in
the gamma frequency range, and disturbance of network dynamics
involving motor cortex were selectively related to motor function.
We present evidence for a plausible mechanism for network
impairment due to epileptic seizures.
Conceived and designed the experiments: GMI TA AO HO SD.
Performed the experiments: GMI SD MLS. Analyzed the data: GMI
TA SD MLS. Contributed reagents/materials/analysis tools: GMI TA
HO AO SD MJT MLS. Wrote the paper: GMI SD OCS JTR EJD MLS.
1. Meador KJ (2002) Cognitive outcomes and predictive factors in epilepsy.
Neurology 58(8 Suppl 5): S21–6.
2. Holmes GL, Ben-Ari Y (1998) Seizures in the developing brain: Perhaps not so
benign after all. Neuron 21(6): 1231–1234.
3. Helmstaedter C, Elger CE (2009) Chronic temporal lobe epilepsy: A
neurodevelopmental or progressively dementing disease? Brain 132(Pt 10):
4. Jonas R, Nguyen S, Hu B, Asarnow RF, LoPresti C, et al. (2004) Cerebral
hemispherectomy: Hospital course, seizure, developmental, language, and motor
outcomes. Neurology 62(10): 1712–1721.
5. Zhang Z, Lu G, Zhong Y, Tan Q, Liao W, et al. (2009) Impaired perceptual
networks in temporal lobe epilepsy revealed by resting fMRI. J Neurol 256(10):
6. Zhang Z, Lu G, Zhong Y, Tan Q, Yang Z, et al. (2009) Impaired attention
network in temporal lobe epilepsy: A resting FMRI study. Neurosci Lett 458(3):
7. Gray CM, Konig P, Engel AK, Singer W (1989) Oscillatory responses in cat
visual cortex exhibit inter-columnar synchronization which reflects global
stimulus properties. Nature 338(6213): 334–337.
8. Engel AK, Singer W (2001) Temporal binding and the neural correlates of
sensory awareness. Trends Cogn Sci 5(1): 16–25.
9. Varela F, Lachaux JP, Rodriguez E, Martinerie J (2001) The brainweb: Phase
synchronization and large-scale integration. Nat Rev Neurosci 2(4): 229–239.
10. Canolty RT, Ganguly K, Kennerley SW, Cadieu CF, Koepsell K, et al. (2010)
Oscillatory phase coupling coordinates anatomically dispersed functional cell
assemblies. Proc Natl Acad Sci U S A 107(40): 17356–17361.
11. van Wingerden M, Vinck M, Lankelma JV, Pennartz CM (2010) Learning-
associated gamma-band phase-locking of action-outcome selective neurons in
orbitofrontal cortex. J Neurosci 30(30): 10025–10038.
12. Schnitzler A, Gross J (2005) Normal and pathological oscillatory communication
in the brain. Nat Rev Neurosci 6(4): 285–296.
13. Benasich AA, Gou Z, Choudhury N, Harris KD (2008) Early cognitive and
language skills are linked to resting frontal gamma power across the first 3 years.
Behav Brain Res 195(2): 215–222.
14. Uhlhaas PJ, Roux F, Singer W, Haenschel C, Sireteanu R, et al. (2009) The
development of neural synchrony reflects late maturation and restructuring of
functional networks in humans. Proc Natl Acad Sci U S A 106(24): 9866–9871.
15. Mazaheri A, Coffey-Corina S, Mangun GR, Bekker EM, Berry AS, et al. (2010)
Functional disconnection of frontal cortex and visual cortex in attention-deficit/
hyperactivity disorder. Biol Psychiatry 67(7): 617–623.
16. Murias M, Webb SJ, Greenson J, Dawson G (2007) Resting state cortical
connectivity reflected in EEG coherence in individuals with autism. Biol
Psychiatry 62(3): 270–273.
17. Wendling F, Chauvel P, Biraben A, Bartolomei F (2010) From intracerebral
EEG signals to brain connectivity: Identification of epileptogenic networks in
partial epilepsy. Front Syst Neurosci 4: 154.
18. Schindler K, Leung H, Elger CE, Lehnertz K (2007) Assessing seizure dynamics
by analysing the correlation structure of multichannel intracranial EEG. Brain
130(Pt 1): 65–77.
19. Warren CP, Hu S, Stead M, Brinkmann BH, Bower MR, et al. (2010)
Synchrony in normal and focal epileptic brain: The seizure onset zone is
functionally disconnected. J Neurophysiol 104(6): 3530–3539.
20. Ortega GJ, Menendez de la Prida L, Sola RG, Pastor J (2008) Synchronization
clusters of interictal activity in the lateral temporal cortex of epileptic patients:
Intraoperative electrocorticographic analysis. Epilepsia 49(2): 269–280.
21. Wilke C, Worrell G, He B (2011) Graph analysis of epileptogenic networks in
human partial epilepsy. Epilepsia 52(1): 84–93.
22. Horstmann MT, Bialonski S, Noennig N, Mai H, Prusseit J, et al. (2010) State
dependent properties of epileptic brain networks: Comparative graph-theoretical
analyses of simultaneously recorded EEG and MEG. Clin Neurophysiol 121(2):
23. Kulandaivel K, Holmes GL (2011) Power spectral analysis in infants with
seizures: Relationship to development. Epilepsy Behav 20(4): 700–705.
24. Blumcke I, Thom M, Aronica E, Armstrong DD, Vinters HV, et al. (2011) The
clinicopathologic spectrum of focal cortical dysplasias: A consensus classification
proposed by an ad hoc task force of the ILAE diagnostic methods commission.
Epilepsia 52(1): 158–174.
25. Benifla M, Sala F,Jr, Jane J, Otsubo H, Ochi A, et al. (2009) Neurosurgical
management of intractable rolandic epilepsy in children: Role of resection in
eloquent cortex. clinical article. J Neurosurg Pediatr 4(3): 199–216.
26. Kemp B, Olivan J (2003) European data format ‘plus’ (EDF+), an EDF alike
standard format for the exchange of physiological data. Clin Neurophysiol
27. Lachaux JP, Rodriguez E, Martinerie J, Varela FJ (1999) Measuring phase
synchrony in brain signals. Hum Brain Mapp 8(4): 194–208.
28. Rubinov M, Sporns O (2010) Complex network measures of brain connectivity:
Uses and interpretations. Neuroimage 52(3): 1059–1069.
29. Newman MEJ (2010) Networks: An introduction. : Oxford University Press.
30. Yu S, Huang D, Singer W, Nikolic D (2008) A small world of neuronal
synchrony. Cereb Cortex 18(12): 2891–2901.
31. Onnela JP, Saramaki J, Kertesz J, Kaski K (2005) Intensity and coherence of
motifs in weighted complex networks. Phys Rev E Stat Nonlin Soft Matter Phys
71(6 Pt 2): 065103.
32. Rubinov M, Sporns O (2010) Complex network measures of brain connectivity:
Uses and interpretations. Neuroimage 52(3): 1059–1069.
33. Matthew C.G., Klove K (1964) Grooved Pegboard Instruction Manual.
University of Wisconsin Medical School, Madison, WI.
34. Reitan R (1969) Manual for Administration of Neuropsychological Test Battery
for Adults and Children Indianapolis, Ind.
35. Weschler D (2003) WISC-IV Administration Manual Manual the Psychological
Corporation San Antonio, TX.
36. Ruff RM, Parker SB (1993) Gender- and age-specific changes in motor speed
and eye-hand coordination in adults: Normative values for the finger tapping
and grooved pegboard tests. Percept Mot Skills 76(3 Pt 2): 1219–1230.
37. Fisher RA (1935) The design of experiments. London, UK: Oliver & Boyd,.
38. Fell J, Klaver P, Lehnertz K, Grunwald T, Schaller C, et al. (2001) Human
memory formation is accompanied by rhinal-hippocampal coupling and
decoupling. Nat Neurosci 4(12): 1259–1264.
39. Burgess AP, Ali L (2002) Functional connectivity of gamma EEG activity is
modulated at low frequency during conscious recollection. Int J Psychophysiol
40. Rodriguez E, George N, Lachaux JP, Martinerie J, Renault B, et al. (1999)
Perception’s shadow: Long-distance synchronization of human brain activity.
Nature 397(6718): 430–433.
41. Rose M, Sommer T, Buchel C (2006) Integration of local features to a global
percept by neural coupling. Cereb Cortex 16(10): 1522–1528.
42. Supp GG, Schlogl A, Trujillo-Barreto N, Muller MM, Gruber T (2007) Directed
cortical information flow during human object recognition: Analyzing induced
EEG gamma-band responses in brain’s source space. PLoS One 2(8): e684.
43. Bartolomei F, Trebuchon A, Gavaret M, Regis J, Wendling F, et al. (2005) Acute
alteration of emotional behaviour in epileptic seizures is related to transient
desynchrony in emotion-regulation networks. Clin Neurophysiol 116(10): 2473–
44. Wang Y, Iliescu BF, Ma J, Josic K, Dragoi V (2011) Adaptive changes in
neuronal synchronization in macaque V4. J Neurosci 31(37): 13204–13213.
45. Masquelier T, Hugues E, Deco G, Thorpe SJ (2009) Oscillations, phase-of-firing
coding, and spike timing-dependent plasticity: An efficient learning scheme.
J Neurosci 29(43): 13484–13493.
46. Miltner WH, Braun C, Arnold M, Witte H, Taub E (1999) Coherence of
gamma-band EEG activity as a basis for associative learning. Nature 397(6718):
Rolandic Disconnection and Motor Deficits
PLoS ONE | www.plosone.org7 June 2012 | Volume 7 | Issue 6 | e39326
47. Fell J, Fernandez G, Klaver P, Axmacher N, Mormann F, et al. (2006) Rhinal- Download full-text
hippocampal coupling during declarative memory formation: Dependence on
item characteristics. Neurosci Lett 407(1): 37–41.
48. Laufs H, Hamandi K, Salek-Haddadi A, Kleinschmidt AK, Duncan JS, et al.
(2007) Temporal lobe interictal epileptic discharges affect cerebral activity in
‘‘default mode’’ brain regions. Hum Brain Mapp 28(10): 1023–1032.
49. Luo C, Li Q, Lai Y, Xia Y, Qin Y, et al. (2011) Altered functional connectivity in
default mode network in absence epilepsy: A resting-state fMRI study. Hum
Brain Mapp 32(3): 438–449.
50. Akiyama T, McCoy B, Go CY, Ochi A, Elliott IM, et al. (2011) Focal resection
of fast ripples on extraoperative intracranial EEG improves seizure outcome in
pediatric epilepsy. Epilepsia 52(10): 1802–1811.
51. Ochi A, Otsubo H, Donner EJ, Elliott I, Iwata R, et al. (2007) Dynamic changes
of ictal high-frequency oscillations in neocortical epilepsy: Using multiple band
frequency analysis. Epilepsia 48(2): 286–296.
52. Nariai H, Nagasawa T, Juhasz C, Sood S, Chugani HT, et al. (2011) Statistical
mapping of ictal high-frequency oscillations in epileptic spasms. Epilepsia 52(1):
Rolandic Disconnection and Motor Deficits
PLoS ONE | www.plosone.org8 June 2012 | Volume 7 | Issue 6 | e39326